Young extracellular vesicles restore burn-induced adipose tissue immunometabolic and mitochondrial function in older mice
Bibliographic record
Abstract
The idea of blood as an elixir of youth led to the classical experiment of heterochronic parabiosis, which demonstrated that young blood could rejuvenate aged tissues in mice. Later, it was discovered that this rejuvenating capacity is due to the presence of circulating extracellular vesicles (EVs), which override deleterious signals from the aged environment via intercellular cues that promote tissue renewal. Aging is associated with alterations in the structure and cargo of EVs with diminished nucleic acid content. We know that aging is a chronological process of progressive cellular and tissue dysfunction that impairs the capacity of older trauma patients to adequately respond to stress, particularly burn trauma, with the adipose tissue serving as the central mediator. Our results demonstrated that, in addition to increasing senescence with chronological aging, burn injury further intensifies the senescence burden in adipose tissue. Notably, EVs from young mouse serum samples significantly reduced burn-induced senescence in aged adipose tissue. We further demonstrated that EVs mitigate lipolysis and are crucial in hepatocellular signaling for the regulation of hepatic inflammation, fat accumulation and dysfunction. Finally, we provide evidence that EV therapy reestablishes immuno-metabolic function, mitochondrial bioenergetics, immune cell infiltration and adipose tissue function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".